Overview

Dataset statistics

Number of variables32
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory235.1 B

Variable types

Numeric20
Text1
Categorical11

Alerts

balance_indicator is highly imbalanced (54.1%)Imbalance
balance_salary_ratio is highly skewed (γ1 = 94.26402235)Skewed
credit_salary_ratio is highly skewed (γ1 = 87.24290193)Skewed
row_number is uniformly distributedUniform
row_number has unique valuesUnique
customer_id has unique valuesUnique
customer_value_normalized has unique valuesUnique
credit_salary_ratio has unique valuesUnique
tenure has 413 (4.1%) zerosZeros
balance has 3617 (36.2%) zerosZeros
balance_sqrt has 3617 (36.2%) zerosZeros
age_balance has 3617 (36.2%) zerosZeros
balance_salary_ratio has 3617 (36.2%) zerosZeros
tenure_age_ratio has 413 (4.1%) zerosZeros

Reproduction

Analysis started2024-05-07 13:28:59.274759
Analysis finished2024-05-07 13:29:17.555618
Duration18.28 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

row_number
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:17.601722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2024-05-07T10:29:17.662787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

customer_id
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690941
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:17.720537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15578824
Q115628528
median15690738
Q315753234
95-th percentile15803034
Maximum15815690
Range249989
Interquartile range (IQR)124705.5

Descriptive statistics

Standard deviation71936.186
Coefficient of variation (CV)0.0045845681
Kurtosis-1.1961125
Mean15690941
Median Absolute Deviation (MAD)62432.5
Skewness0.0011491459
Sum1.5690941 × 1011
Variance5.1748149 × 109
MonotonicityNot monotonic
2024-05-07T10:29:17.781060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15634602 1
 
< 0.1%
15667932 1
 
< 0.1%
15766185 1
 
< 0.1%
15667632 1
 
< 0.1%
15599024 1
 
< 0.1%
15798709 1
 
< 0.1%
15741921 1
 
< 0.1%
15793671 1
 
< 0.1%
15797900 1
 
< 0.1%
15795933 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
15565701 1
< 0.1%
15565706 1
< 0.1%
15565714 1
< 0.1%
15565779 1
< 0.1%
15565796 1
< 0.1%
15565806 1
< 0.1%
15565878 1
< 0.1%
15565879 1
< 0.1%
15565891 1
< 0.1%
15565996 1
< 0.1%
ValueCountFrequency (%)
15815690 1
< 0.1%
15815660 1
< 0.1%
15815656 1
< 0.1%
15815645 1
< 0.1%
15815628 1
< 0.1%
15815626 1
< 0.1%
15815615 1
< 0.1%
15815560 1
< 0.1%
15815552 1
< 0.1%
15815534 1
< 0.1%
Distinct2932
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:17.967044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4349
Min length2

Characters and Unicode

Total characters64349
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)15.6%

Sample

1st rowHargrave
2nd rowHill
3rd rowOnio
4th rowBoni
5th rowMitchell
ValueCountFrequency (%)
lo 33
 
0.3%
smith 32
 
0.3%
martin 29
 
0.3%
scott 29
 
0.3%
walker 28
 
0.3%
brown 26
 
0.3%
yeh 25
 
0.2%
shih 25
 
0.2%
genovese 25
 
0.2%
maclean 24
 
0.2%
Other values (2931) 9779
97.3%
2024-05-07T10:29:18.212442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

credit_score
Real number (ℝ)

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:18.294602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2024-05-07T10:29:18.350262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 233
 
2.3%
678 63
 
0.6%
655 54
 
0.5%
705 53
 
0.5%
667 53
 
0.5%
684 52
 
0.5%
670 50
 
0.5%
651 50
 
0.5%
683 48
 
0.5%
652 48
 
0.5%
Other values (450) 9296
93.0%
ValueCountFrequency (%)
350 5
0.1%
351 1
 
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
 
< 0.1%
382 1
 
< 0.1%
ValueCountFrequency (%)
850 233
2.3%
849 8
 
0.1%
848 5
 
0.1%
847 6
 
0.1%
846 5
 
0.1%
845 6
 
0.1%
844 7
 
0.1%
843 2
 
< 0.1%
842 7
 
0.1%
841 12
 
0.1%

geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
France
5014 
Germany
2509 
Spain
2477 

Length

Max length7
Median length6
Mean length6.0032
Min length5

Characters and Unicode

Total characters60032
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSpain
3rd rowFrance
4th rowFrance
5th rowSpain

Common Values

ValueCountFrequency (%)
France 5014
50.1%
Germany 2509
25.1%
Spain 2477
24.8%

Length

2024-05-07T10:29:18.406167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:18.455499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
france 5014
50.1%
germany 2509
25.1%
spain 2477
24.8%

Most occurring characters

ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Male
5457 
Female
4543 

Length

Max length6
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5457
54.6%
Female 4543
45.4%

Length

2024-05-07T10:29:18.506117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:18.548171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 5457
54.6%
female 4543
45.4%

Most occurring characters

ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:18.594709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2024-05-07T10:29:18.653129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 478
 
4.8%
38 477
 
4.8%
35 474
 
4.7%
36 456
 
4.6%
34 447
 
4.5%
33 442
 
4.4%
40 432
 
4.3%
39 423
 
4.2%
32 418
 
4.2%
31 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
18 22
 
0.2%
19 27
 
0.3%
20 40
 
0.4%
21 53
 
0.5%
22 84
0.8%
23 99
1.0%
24 132
1.3%
25 154
1.5%
26 200
2.0%
27 209
2.1%
ValueCountFrequency (%)
92 2
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%
80 3
< 0.1%
79 4
< 0.1%
78 5
0.1%

tenure
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:18.704348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2024-05-07T10:29:18.780661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1048
10.5%
1 1035
10.3%
7 1028
10.3%
8 1025
10.2%
5 1012
10.1%
3 1009
10.1%
4 989
9.9%
9 984
9.8%
6 967
9.7%
10 490
4.9%
ValueCountFrequency (%)
0 413
 
4.1%
1 1035
10.3%
2 1048
10.5%
3 1009
10.1%
4 989
9.9%
5 1012
10.1%
6 967
9.7%
7 1028
10.3%
8 1025
10.2%
9 984
9.8%
ValueCountFrequency (%)
10 490
4.9%
9 984
9.8%
8 1025
10.2%
7 1028
10.3%
6 967
9.7%
5 1012
10.1%
4 989
9.9%
3 1009
10.1%
2 1048
10.5%
1 1035
10.3%

balance
Real number (ℝ)

ZEROS 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:18.845806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2024-05-07T10:29:18.905978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
130170.82 2
 
< 0.1%
105473.74 2
 
< 0.1%
85304.27 1
 
< 0.1%
159397.75 1
 
< 0.1%
144238.7 1
 
< 0.1%
112262.84 1
 
< 0.1%
109106.8 1
 
< 0.1%
142147.32 1
 
< 0.1%
109109.33 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 1
< 0.1%
238387.56 1
< 0.1%
222267.63 1
< 0.1%
221532.8 1
< 0.1%
216109.88 1
< 0.1%
214346.96 1
< 0.1%
213146.2 1
< 0.1%
212778.2 1
< 0.1%
212696.32 1
< 0.1%
212692.97 1
< 0.1%

num_of_products
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Length

2024-05-07T10:29:18.956289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:18.995610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

has_cr_card
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Length

2024-05-07T10:29:19.039819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:19.077768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring characters

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

is_active_member
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Length

2024-05-07T10:29:19.119243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:19.156119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

estimated_salary
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:19.205579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2024-05-07T10:29:19.267234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.92 2
 
< 0.1%
101348.88 1
 
< 0.1%
55313.44 1
 
< 0.1%
72500.68 1
 
< 0.1%
182692.8 1
 
< 0.1%
4993.94 1
 
< 0.1%
124964.82 1
 
< 0.1%
161971.42 1
 
< 0.1%
39488.04 1
 
< 0.1%
187811.71 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
11.58 1
< 0.1%
90.07 1
< 0.1%
91.75 1
< 0.1%
96.27 1
< 0.1%
106.67 1
< 0.1%
123.07 1
< 0.1%
142.81 1
< 0.1%
143.34 1
< 0.1%
178.19 1
< 0.1%
216.27 1
< 0.1%
ValueCountFrequency (%)
199992.48 1
< 0.1%
199970.74 1
< 0.1%
199953.33 1
< 0.1%
199929.17 1
< 0.1%
199909.32 1
< 0.1%
199862.75 1
< 0.1%
199857.47 1
< 0.1%
199841.32 1
< 0.1%
199808.1 1
< 0.1%
199805.63 1
< 0.1%

exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7963 
1
2037 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Length

2024-05-07T10:29:19.322641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:19.359244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7963
79.6%
1 2037
 
20.4%

age_squared
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1624.8896
Minimum324
Maximum8464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:19.406435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum324
5-th percentile625
Q11024
median1369
Q31936
95-th percentile3600
Maximum8464
Range8140
Interquartile range (IQR)912

Descriptive statistics

Standard deviation942.96316
Coefficient of variation (CV)0.58032445
Kurtosis4.8610635
Mean1624.8896
Median Absolute Deviation (MAD)408
Skewness1.8952596
Sum16248896
Variance889179.52
MonotonicityNot monotonic
2024-05-07T10:29:19.469046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1369 478
 
4.8%
1444 477
 
4.8%
1225 474
 
4.7%
1296 456
 
4.6%
1156 447
 
4.5%
1089 442
 
4.4%
1600 432
 
4.3%
1521 423
 
4.2%
1024 418
 
4.2%
961 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
324 22
 
0.2%
361 27
 
0.3%
400 40
 
0.4%
441 53
 
0.5%
484 84
0.8%
529 99
1.0%
576 132
1.3%
625 154
1.5%
676 200
2.0%
729 209
2.1%
ValueCountFrequency (%)
8464 2
 
< 0.1%
7744 1
 
< 0.1%
7225 1
 
< 0.1%
7056 2
 
< 0.1%
6889 1
 
< 0.1%
6724 1
 
< 0.1%
6561 4
< 0.1%
6400 3
< 0.1%
6241 4
< 0.1%
6084 5
0.1%

balance_sqrt
Real number (ℝ)

ZEROS 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.08043
Minimum0
Maximum500.89728
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:19.530054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median311.7668
Q3357.27334
95-th percentile403.37534
Maximum500.89728
Range500.89728
Interquartile range (IQR)357.27334

Descriptive statistics

Standard deviation168.79723
Coefficient of variation (CV)0.77048062
Kurtosis-1.6441198
Mean219.08043
Median Absolute Deviation (MAD)69.129147
Skewness-0.44277468
Sum2190804.3
Variance28492.504
MonotonicityNot monotonic
2024-05-07T10:29:19.661308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
360.7919345 2
 
< 0.1%
324.7672089 2
 
< 0.1%
292.0689473 1
 
< 0.1%
399.2464778 1
 
< 0.1%
379.7877039 1
 
< 0.1%
335.0564729 1
 
< 0.1%
330.3131847 1
 
< 0.1%
377.0242963 1
 
< 0.1%
330.3170144 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
61.38965711 1
 
< 0.1%
111.6207418 1
 
< 0.1%
119.4269651 1
 
< 0.1%
129.9753438 1
 
< 0.1%
153.3078928 1
 
< 0.1%
155.0595047 1
 
< 0.1%
165.1921003 1
 
< 0.1%
165.8829407 1
 
< 0.1%
166.6012305 1
 
< 0.1%
ValueCountFrequency (%)
500.8972849 1
< 0.1%
488.2494854 1
< 0.1%
471.4526806 1
< 0.1%
470.6727101 1
< 0.1%
464.8761986 1
< 0.1%
462.9761981 1
< 0.1%
461.6775931 1
< 0.1%
461.2788744 1
< 0.1%
461.1901126 1
< 0.1%
461.1864807 1
< 0.1%
Distinct1096
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean996.1271
Minimum350
Maximum3400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:19.716517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile520
Q1649
median850
Q31318.5
95-th percentile1634
Maximum3400
Range3050
Interquartile range (IQR)669.5

Descriptive statistics

Standard deviation411.51827
Coefficient of variation (CV)0.41311823
Kurtosis1.0442431
Mean996.1271
Median Absolute Deviation (MAD)288
Skewness0.8817746
Sum9961271
Variance169347.29
MonotonicityNot monotonic
2024-05-07T10:29:19.773487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 124
 
1.2%
1700 108
 
1.1%
678 32
 
0.3%
1356 31
 
0.3%
640 30
 
0.3%
637 30
 
0.3%
705 30
 
0.3%
682 30
 
0.3%
684 28
 
0.3%
655 28
 
0.3%
Other values (1086) 9529
95.3%
ValueCountFrequency (%)
350 4
< 0.1%
351 1
 
< 0.1%
359 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 1
 
< 0.1%
382 1
 
< 0.1%
383 1
 
< 0.1%
386 1
 
< 0.1%
ValueCountFrequency (%)
3400 2
< 0.1%
3388 1
< 0.1%
3368 1
< 0.1%
3276 1
< 0.1%
3188 1
< 0.1%
3168 1
< 0.1%
3092 1
< 0.1%
3084 1
< 0.1%
3028 1
< 0.1%
2988 1
< 0.1%

age_balance
Real number (ℝ)

ZEROS 

Distinct6383
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2995492
Minimum0
Maximum13796953
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:19.830641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3360210.8
Q34911035.1
95-th percentile7308972.6
Maximum13796953
Range13796953
Interquartile range (IQR)4911035.1

Descriptive statistics

Standard deviation2646812.7
Coefficient of variation (CV)0.88359865
Kurtosis-0.68593908
Mean2995492
Median Absolute Deviation (MAD)2508157.5
Skewness0.333125
Sum2.995492 × 1010
Variance7.0056173 × 1012
MonotonicityNot monotonic
2024-05-07T10:29:19.892061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
4621662.9 2
 
< 0.1%
4544206.3 1
 
< 0.1%
4813426.73 1
 
< 0.1%
4161915 1
 
< 0.1%
3761550.3 1
 
< 0.1%
5100728 1
 
< 0.1%
3750206.2 1
 
< 0.1%
4041462.24 1
 
< 0.1%
3927844.8 1
 
< 0.1%
Other values (6373) 6373
63.7%
ValueCountFrequency (%)
0 3617
36.2%
150747.6 1
 
< 0.1%
499198 1
 
< 0.1%
573122.74 1
 
< 0.1%
709530.78 1
 
< 0.1%
750575.49 1
 
< 0.1%
804923.13 1
 
< 0.1%
829147.68 1
 
< 0.1%
965392.8 1
 
< 0.1%
965929.2 1
 
< 0.1%
ValueCountFrequency (%)
13796952.94 1
< 0.1%
13588090.92 1
< 0.1%
13002612.69 1
< 0.1%
12903905.97 1
< 0.1%
12678403.62 1
< 0.1%
12534373.04 1
< 0.1%
12481756.32 1
< 0.1%
12427644.1 1
< 0.1%
12234455.68 1
< 0.1%
12087895.55 1
< 0.1%

engagement_score
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77524
Minimum0.2
Maximum1.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:19.943128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.5
median0.7
Q31
95-th percentile1.2
Maximum1.6
Range1.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.30746121
Coefficient of variation (CV)0.39660132
Kurtosis-0.93036175
Mean0.77524
Median Absolute Deviation (MAD)0.3
Skewness-0.072674589
Sum7752.4
Variance0.094532396
MonotonicityNot monotonic
2024-05-07T10:29:19.987632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.7 2299
23.0%
0.5 1803
18.0%
1 1775
17.8%
1.2 1735
17.3%
0.2 718
 
7.2%
0.9 711
 
7.1%
0.4 633
 
6.3%
0.9 112
 
1.1%
1.4 78
 
0.8%
1.1 57
 
0.6%
Other values (4) 79
 
0.8%
ValueCountFrequency (%)
0.2 718
 
7.2%
0.4 633
 
6.3%
0.5 1803
18.0%
0.6 41
 
0.4%
0.7 2299
23.0%
0.8 9
 
0.1%
0.9 711
 
7.1%
0.9 112
 
1.1%
1 1775
17.8%
1.1 57
 
0.6%
ValueCountFrequency (%)
1.6 19
 
0.2%
1.4 78
 
0.8%
1.3 10
 
0.1%
1.2 1735
17.3%
1.1 57
 
0.6%
1 1775
17.8%
0.9 112
 
1.1%
0.9 711
 
7.1%
0.8 9
 
0.1%
0.7 2299
23.0%

customer_value_normalized
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1878684
Minimum0.25053337
Maximum2.5078665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:20.042556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.25053337
5-th percentile0.56706077
Q10.90929614
median1.1841386
Q31.4587005
95-th percentile1.8267015
Maximum2.5078665
Range2.2573331
Interquartile range (IQR)0.54940432

Descriptive statistics

Standard deviation0.38302131
Coefficient of variation (CV)0.32244422
Kurtosis-0.4176198
Mean1.1878684
Median Absolute Deviation (MAD)0.27466217
Skewness0.089118267
Sum11878.684
Variance0.14670532
MonotonicityNot monotonic
2024-05-07T10:29:20.099469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7567634543 1
 
< 0.1%
0.7765775993 1
 
< 0.1%
1.283641359 1
 
< 0.1%
0.8625170306 1
 
< 0.1%
1.413498348 1
 
< 0.1%
0.709845731 1
 
< 0.1%
1.124847594 1
 
< 0.1%
1.059887552 1
 
< 0.1%
1.014001638 1
 
< 0.1%
1.43909386 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.2505333701 1
< 0.1%
0.2508909835 1
< 0.1%
0.2529130595 1
< 0.1%
0.2534543299 1
< 0.1%
0.2535260326 1
< 0.1%
0.2535326328 1
< 0.1%
0.2540354517 1
< 0.1%
0.2547222276 1
< 0.1%
0.2552518475 1
< 0.1%
0.2563143874 1
< 0.1%
ValueCountFrequency (%)
2.507866453 1
< 0.1%
2.433600182 1
< 0.1%
2.431669232 1
< 0.1%
2.412930236 1
< 0.1%
2.411784805 1
< 0.1%
2.377817612 1
< 0.1%
2.377623301 1
< 0.1%
2.360634035 1
< 0.1%
2.346259384 1
< 0.1%
2.331314352 1
< 0.1%

product_density
Real number (ℝ)

Distinct6386
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645500
Minimum4.1948498 × 10-6
Maximum4000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:20.156019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.1948498 × 10-6
5-th percentile6.5076107 × 10-6
Q18.8766719 × 10-6
median1.6348888 × 10-5
Q32000000
95-th percentile2000000
Maximum4000000
Range4000000
Interquartile range (IQR)2000000

Descriptive statistics

Standard deviation908026.54
Coefficient of variation (CV)1.4067026
Kurtosis-0.84314567
Mean645500
Median Absolute Deviation (MAD)9.1478152 × 10-6
Skewness0.88079002
Sum6.455 × 109
Variance8.245122 × 1011
MonotonicityNot monotonic
2024-05-07T10:29:20.219461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000 2600
26.0%
1000000 905
 
9.0%
3000000 98
 
1.0%
4000000 14
 
0.1%
9.481032909 × 10-62
 
< 0.1%
1.182202183 × 10-51
 
< 0.1%
1.441644051 × 10-51
 
< 0.1%
6.273614276 × 10-61
 
< 0.1%
6.932952113 × 10-61
 
< 0.1%
8.907667043 × 10-61
 
< 0.1%
Other values (6376) 6376
63.8%
ValueCountFrequency (%)
4.194849765 × 10-61
< 0.1%
4.49908068 × 10-61
< 0.1%
4.514004247 × 10-61
< 0.1%
4.627275717 × 10-61
< 0.1%
4.691615426 × 10-61
< 0.1%
4.699729578 × 10-61
< 0.1%
4.701538795 × 10-61
< 0.1%
4.701612846 × 10-61
< 0.1%
4.710004327 × 10-61
< 0.1%
4.722008066 × 10-61
< 0.1%
ValueCountFrequency (%)
4000000 14
 
0.1%
3000000 98
 
1.0%
2000000 2600
26.0%
1000000 905
 
9.0%
0.0005306883823 1
 
< 0.1%
0.0001402249208 1
 
< 0.1%
8.534165357 × 10-51
 
< 0.1%
8.02620395 × 10-51
 
< 0.1%
7.268194562 × 10-51
 
< 0.1%
6.853593087 × 10-51
 
< 0.1%

balance_salary_ratio
Real number (ℝ)

SKEWED  ZEROS 

Distinct6384
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8787029
Minimum0
Maximum10614.655
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:20.282038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.74700231
Q31.514022
95-th percentile7.2495003
Maximum10614.655
Range10614.655
Interquartile range (IQR)1.514022

Descriptive statistics

Standard deviation108.33726
Coefficient of variation (CV)27.931311
Kurtosis9208.1391
Mean3.8787029
Median Absolute Deviation (MAD)0.74700231
Skewness94.264022
Sum38787.029
Variance11736.962
MonotonicityNot monotonic
2024-05-07T10:29:20.340635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
1.394096807 1
 
< 0.1%
6.742188168 1
 
< 0.1%
1.835961263 1
 
< 0.1%
2.780685754 1
 
< 0.1%
0.7822065495 1
 
< 0.1%
8.720534374 1
 
< 0.1%
0.8993965269 1
 
< 0.1%
3.599756281 1
 
< 0.1%
21.8483462 1
 
< 0.1%
Other values (6374) 6374
63.7%
ValueCountFrequency (%)
0 3617
36.2%
0.02128418881 1
 
< 0.1%
0.07946553593 1
 
< 0.1%
0.1383670781 1
 
< 0.1%
0.1416141163 1
 
< 0.1%
0.1809964844 1
 
< 0.1%
0.1875142498 1
 
< 0.1%
0.1925817779 1
 
< 0.1%
0.2009926374 1
 
< 0.1%
0.2053787413 1
 
< 0.1%
ValueCountFrequency (%)
10614.65544 1
< 0.1%
1326.102779 1
< 0.1%
856.0641099 1
< 0.1%
611.268941 1
< 0.1%
437.9808416 1
< 0.1%
353.2296248 1
< 0.1%
349.5219873 1
< 0.1%
345.3423327 1
< 0.1%
321.3587687 1
< 0.1%
291.1422313 1
< 0.1%

credit_score_age_ratio
Real number (ℝ)

Distinct6112
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.87434
Minimum4.8571429
Maximum46.888889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:20.396097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.8571429
5-th percentile10.166667
Q114.088889
median17.285714
Q320.961538
95-th percentile27.615865
Maximum46.888889
Range42.031746
Interquartile range (IQR)6.8726496

Descriptive statistics

Standard deviation5.376363
Coefficient of variation (CV)0.30078666
Kurtosis1.0228445
Mean17.87434
Median Absolute Deviation (MAD)3.4017857
Skewness0.76051669
Sum178743.4
Variance28.905279
MonotonicityNot monotonic
2024-05-07T10:29:20.451185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 25
 
0.2%
20 22
 
0.2%
16 20
 
0.2%
18 18
 
0.2%
21.25 17
 
0.2%
25 16
 
0.2%
13 16
 
0.2%
19 15
 
0.1%
27.41935484 15
 
0.1%
15 15
 
0.1%
Other values (6102) 9821
98.2%
ValueCountFrequency (%)
4.857142857 1
< 0.1%
5.829545455 1
< 0.1%
5.833333333 1
< 0.1%
6.112676056 1
< 0.1%
6.157894737 1
< 0.1%
6.22972973 1
< 0.1%
6.392857143 1
< 0.1%
6.481481481 1
< 0.1%
6.515151515 1
< 0.1%
6.651515152 1
< 0.1%
ValueCountFrequency (%)
46.88888889 1
< 0.1%
46.38888889 1
< 0.1%
44.77777778 1
< 0.1%
44.73684211 1
< 0.1%
42.83333333 1
< 0.1%
42.5 1
< 0.1%
41.75 1
< 0.1%
41.68421053 1
< 0.1%
41.10526316 1
< 0.1%
41 1
< 0.1%

tenure_age_ratio
Real number (ℝ)

ZEROS 

Distinct414
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13793635
Minimum0
Maximum0.55555556
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:20.504451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01754386
Q10.064516129
median0.12903226
Q30.2
95-th percentile0.29411765
Maximum0.55555556
Range0.55555556
Interquartile range (IQR)0.13548387

Descriptive statistics

Standard deviation0.089505697
Coefficient of variation (CV)0.64889129
Kurtosis-0.044717912
Mean0.13793635
Median Absolute Deviation (MAD)0.066619916
Skewness0.56614673
Sum1379.3635
Variance0.0080112699
MonotonicityNot monotonic
2024-05-07T10:29:20.561616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
4.1%
0.2 188
 
1.9%
0.1666666667 160
 
1.6%
0.1428571429 149
 
1.5%
0.25 139
 
1.4%
0.125 124
 
1.2%
0.1538461538 109
 
1.1%
0.1 104
 
1.0%
0.1111111111 103
 
1.0%
0.1818181818 85
 
0.9%
Other values (404) 8426
84.3%
ValueCountFrequency (%)
0 413
4.1%
0.01086956522 1
 
< 0.1%
0.01234567901 1
 
< 0.1%
0.01298701299 1
 
< 0.1%
0.01333333333 1
 
< 0.1%
0.01351351351 1
 
< 0.1%
0.01369863014 2
 
< 0.1%
0.01388888889 2
 
< 0.1%
0.01408450704 2
 
< 0.1%
0.01428571429 4
 
< 0.1%
ValueCountFrequency (%)
0.5555555556 2
 
< 0.1%
0.5 2
 
< 0.1%
0.4761904762 3
 
< 0.1%
0.4736842105 4
 
< 0.1%
0.4545454545 5
0.1%
0.45 3
 
< 0.1%
0.4444444444 2
 
< 0.1%
0.4347826087 10
0.1%
0.4285714286 4
 
< 0.1%
0.4210526316 3
 
< 0.1%

credit_salary_ratio
Real number (ℝ)

SKEWED  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033709569
Minimum0.0018231684
Maximum61.226252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:20.618126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0018231684
5-th percentile0.0031566665
Q10.0043502349
median0.0064731469
Q30.01281059
95-th percentile0.065936344
Maximum61.226252
Range61.224429
Interquartile range (IQR)0.0084603556

Descriptive statistics

Standard deviation0.64221876
Coefficient of variation (CV)19.051527
Kurtosis8252.0618
Mean0.033709569
Median Absolute Deviation (MAD)0.0026840634
Skewness87.242902
Sum337.09569
Variance0.41244494
MonotonicityNot monotonic
2024-05-07T10:29:20.675545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006107615595 1
 
< 0.1%
0.01370372192 1
 
< 0.1%
0.009457598914 1
 
< 0.1%
0.009696460778 1
 
< 0.1%
0.002769676747 1
 
< 0.1%
0.1177427042 1
 
< 0.1%
0.00497740084 1
 
< 0.1%
0.003741400798 1
 
< 0.1%
0.01309257183 1
 
< 0.1%
0.003604674064 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.001823168397 1
< 0.1%
0.002031090659 1
< 0.1%
0.002069310829 1
< 0.1%
0.002127650924 1
< 0.1%
0.002154884804 1
< 0.1%
0.002164633998 1
< 0.1%
0.002173955152 1
< 0.1%
0.002207422047 1
< 0.1%
0.002207951468 1
< 0.1%
0.002263401272 1
< 0.1%
ValueCountFrequency (%)
61.22625216 1
< 0.1%
7.37509089 1
< 0.1%
7.029972752 1
< 0.1%
6.939047408 1
< 0.1%
5.896690728 1
< 0.1%
5.752823596 1
< 0.1%
5.027659128 1
< 0.1%
4.551321623 1
< 0.1%
4.332356635 1
< 0.1%
2.623706339 1
< 0.1%

balance_indicator
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
low
9031 
high
969 

Length

Max length4
Median length3
Mean length3.0969
Min length3

Characters and Unicode

Total characters30969
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow
2nd rowlow
3rd rowhigh
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
low 9031
90.3%
high 969
 
9.7%

Length

2024-05-07T10:29:20.728544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:20.766065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 9031
90.3%
high 969
 
9.7%

Most occurring characters

ValueCountFrequency (%)
l 9031
29.2%
o 9031
29.2%
w 9031
29.2%
h 1938
 
6.3%
i 969
 
3.1%
g 969
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 9031
29.2%
o 9031
29.2%
w 9031
29.2%
h 1938
 
6.3%
i 969
 
3.1%
g 969
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 9031
29.2%
o 9031
29.2%
w 9031
29.2%
h 1938
 
6.3%
i 969
 
3.1%
g 969
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 9031
29.2%
o 9031
29.2%
w 9031
29.2%
h 1938
 
6.3%
i 969
 
3.1%
g 969
 
3.1%

life_stage
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
middle_age
4586 
adulthood
4064 
senior
1261 
adolescence
 
89

Length

Max length11
Median length10
Mean length9.0981
Min length6

Characters and Unicode

Total characters90981
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmiddle_age
2nd rowmiddle_age
3rd rowmiddle_age
4th rowmiddle_age
5th rowmiddle_age

Common Values

ValueCountFrequency (%)
middle_age 4586
45.9%
adulthood 4064
40.6%
senior 1261
 
12.6%
adolescence 89
 
0.9%

Length

2024-05-07T10:29:20.808924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:20.849981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
middle_age 4586
45.9%
adulthood 4064
40.6%
senior 1261
 
12.6%
adolescence 89
 
0.9%

Most occurring characters

ValueCountFrequency (%)
d 17389
19.1%
e 10700
11.8%
o 9478
10.4%
l 8739
9.6%
a 8739
9.6%
i 5847
 
6.4%
m 4586
 
5.0%
_ 4586
 
5.0%
g 4586
 
5.0%
u 4064
 
4.5%
Other values (6) 12267
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 17389
19.1%
e 10700
11.8%
o 9478
10.4%
l 8739
9.6%
a 8739
9.6%
i 5847
 
6.4%
m 4586
 
5.0%
_ 4586
 
5.0%
g 4586
 
5.0%
u 4064
 
4.5%
Other values (6) 12267
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 17389
19.1%
e 10700
11.8%
o 9478
10.4%
l 8739
9.6%
a 8739
9.6%
i 5847
 
6.4%
m 4586
 
5.0%
_ 4586
 
5.0%
g 4586
 
5.0%
u 4064
 
4.5%
Other values (6) 12267
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 17389
19.1%
e 10700
11.8%
o 9478
10.4%
l 8739
9.6%
a 8739
9.6%
i 5847
 
6.4%
m 4586
 
5.0%
_ 4586
 
5.0%
g 4586
 
5.0%
u 4064
 
4.5%
Other values (6) 12267
13.5%

cs_category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
medium
6241 
high
3116 
low
643 

Length

Max length6
Median length6
Mean length5.1839
Min length3

Characters and Unicode

Total characters51839
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium
2nd rowmedium
3rd rowmedium
4th rowmedium
5th rowhigh

Common Values

ValueCountFrequency (%)
medium 6241
62.4%
high 3116
31.2%
low 643
 
6.4%

Length

2024-05-07T10:29:20.899845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:20.942262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
medium 6241
62.4%
high 3116
31.2%
low 643
 
6.4%

Most occurring characters

ValueCountFrequency (%)
m 12482
24.1%
i 9357
18.1%
e 6241
12.0%
d 6241
12.0%
u 6241
12.0%
h 6232
12.0%
g 3116
 
6.0%
l 643
 
1.2%
o 643
 
1.2%
w 643
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51839
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 12482
24.1%
i 9357
18.1%
e 6241
12.0%
d 6241
12.0%
u 6241
12.0%
h 6232
12.0%
g 3116
 
6.0%
l 643
 
1.2%
o 643
 
1.2%
w 643
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51839
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 12482
24.1%
i 9357
18.1%
e 6241
12.0%
d 6241
12.0%
u 6241
12.0%
h 6232
12.0%
g 3116
 
6.0%
l 643
 
1.2%
o 643
 
1.2%
w 643
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51839
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 12482
24.1%
i 9357
18.1%
e 6241
12.0%
d 6241
12.0%
u 6241
12.0%
h 6232
12.0%
g 3116
 
6.0%
l 643
 
1.2%
o 643
 
1.2%
w 643
 
1.2%

tenure_group
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
long_standing
3527 
new
3505 
intermediate
2968 

Length

Max length13
Median length12
Mean length9.1982
Min length3

Characters and Unicode

Total characters91982
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownew
2nd rownew
3rd rowlong_standing
4th rownew
5th rownew

Common Values

ValueCountFrequency (%)
long_standing 3527
35.3%
new 3505
35.0%
intermediate 2968
29.7%

Length

2024-05-07T10:29:20.985691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:21.025682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
long_standing 3527
35.3%
new 3505
35.0%
intermediate 2968
29.7%

Most occurring characters

ValueCountFrequency (%)
n 17054
18.5%
e 12409
13.5%
t 9463
10.3%
i 9463
10.3%
g 7054
7.7%
a 6495
 
7.1%
d 6495
 
7.1%
l 3527
 
3.8%
o 3527
 
3.8%
_ 3527
 
3.8%
Other values (4) 12968
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 17054
18.5%
e 12409
13.5%
t 9463
10.3%
i 9463
10.3%
g 7054
7.7%
a 6495
 
7.1%
d 6495
 
7.1%
l 3527
 
3.8%
o 3527
 
3.8%
_ 3527
 
3.8%
Other values (4) 12968
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 17054
18.5%
e 12409
13.5%
t 9463
10.3%
i 9463
10.3%
g 7054
7.7%
a 6495
 
7.1%
d 6495
 
7.1%
l 3527
 
3.8%
o 3527
 
3.8%
_ 3527
 
3.8%
Other values (4) 12968
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 17054
18.5%
e 12409
13.5%
t 9463
10.3%
i 9463
10.3%
g 7054
7.7%
a 6495
 
7.1%
d 6495
 
7.1%
l 3527
 
3.8%
o 3527
 
3.8%
_ 3527
 
3.8%
Other values (4) 12968
14.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
650.2768920652373
5457 
650.831388950033
4543 

Length

Max length17
Median length17
Mean length16.5457
Min length16

Characters and Unicode

Total characters165457
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row650.831388950033
2nd row650.831388950033
3rd row650.831388950033
4th row650.831388950033
5th row650.831388950033

Common Values

ValueCountFrequency (%)
650.2768920652373 5457
54.6%
650.831388950033 4543
45.4%

Length

2024-05-07T10:29:21.071574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T10:29:21.110038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
650.2768920652373 5457
54.6%
650.831388950033 4543
45.4%

Most occurring characters

ValueCountFrequency (%)
3 29086
17.6%
0 24543
14.8%
6 20914
12.6%
5 20000
12.1%
8 19086
11.5%
2 16371
9.9%
7 10914
 
6.6%
. 10000
 
6.0%
9 10000
 
6.0%
1 4543
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 165457
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 29086
17.6%
0 24543
14.8%
6 20914
12.6%
5 20000
12.1%
8 19086
11.5%
2 16371
9.9%
7 10914
 
6.6%
. 10000
 
6.0%
9 10000
 
6.0%
1 4543
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 165457
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 29086
17.6%
0 24543
14.8%
6 20914
12.6%
5 20000
12.1%
8 19086
11.5%
2 16371
9.9%
7 10914
 
6.6%
. 10000
 
6.0%
9 10000
 
6.0%
1 4543
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 165457
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 29086
17.6%
0 24543
14.8%
6 20914
12.6%
5 20000
12.1%
8 19086
11.5%
2 16371
9.9%
7 10914
 
6.6%
. 10000
 
6.0%
9 10000
 
6.0%
1 4543
 
2.7%

balance_age
Real number (ℝ)

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum123794.77
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:21.159070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68990.37
Q172824.278
median76038.89
Q379573.747
95-th percentile86552.355
Maximum123794.77
Range123794.77
Interquartile range (IQR)6749.4693

Descriptive statistics

Standard deviation5670.4084
Coefficient of variation (CV)0.07413666
Kurtosis25.710165
Mean76485.889
Median Absolute Deviation (MAD)3214.6118
Skewness-1.1744445
Sum7.6485889 × 108
Variance32153531
MonotonicityNot monotonic
2024-05-07T10:29:21.293961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80054.66659 478
 
4.8%
75272.59937 477
 
4.8%
77410.40671 474
 
4.7%
72727.08789 456
 
4.6%
71054.62973 447
 
4.5%
76448.98753 442
 
4.4%
77025.66595 432
 
4.3%
74285.28903 423
 
4.2%
75696.78337 418
 
4.2%
75503.48604 404
 
4.0%
Other values (57) 5549
55.5%
ValueCountFrequency (%)
0 6
 
0.1%
30507.2875 4
 
< 0.1%
42472.4475 4
 
< 0.1%
46915.795 10
 
0.1%
54072.39 9
 
0.1%
57537.85185 27
 
0.3%
62514.71686 35
0.4%
65528.92889 18
 
0.2%
68213.74881 84
0.8%
68884.216 75
0.8%
ValueCountFrequency (%)
123794.775 2
 
< 0.1%
123356.63 1
 
< 0.1%
113091.0618 11
 
0.1%
92823.6155 40
 
0.4%
90510.55211 19
 
0.2%
90057.865 2
 
< 0.1%
89967.04086 70
0.7%
89835.28134 67
0.7%
87894.03784 74
0.7%
87453.24387 119
1.2%

salary_rank_geography
Real number (ℝ)

Distinct5014
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1879.0403
Minimum1
Maximum5014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-07T10:29:21.352139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile167
Q1834
median1667
Q32514.25
95-th percentile4514.05
Maximum5014
Range5013
Interquartile range (IQR)1680.25

Descriptive statistics

Standard deviation1306.174
Coefficient of variation (CV)0.69512823
Kurtosis-0.43342096
Mean1879.0403
Median Absolute Deviation (MAD)838
Skewness0.66857119
Sum18790403
Variance1706090.4
MonotonicityNot monotonic
2024-05-07T10:29:21.412151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2381 3
 
< 0.1%
1100 3
 
< 0.1%
1132 3
 
< 0.1%
856 3
 
< 0.1%
1354 3
 
< 0.1%
469 3
 
< 0.1%
1620 3
 
< 0.1%
757 3
 
< 0.1%
1217 3
 
< 0.1%
292 3
 
< 0.1%
Other values (5004) 9970
99.7%
ValueCountFrequency (%)
1 3
< 0.1%
2 3
< 0.1%
3 3
< 0.1%
4 3
< 0.1%
5 3
< 0.1%
6 3
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 3
< 0.1%
10 3
< 0.1%
ValueCountFrequency (%)
5014 1
< 0.1%
5013 1
< 0.1%
5012 1
< 0.1%
5011 1
< 0.1%
5010 1
< 0.1%
5009 1
< 0.1%
5008 1
< 0.1%
5007 1
< 0.1%
5006 1
< 0.1%
5005 1
< 0.1%

Interactions

2024-05-07T10:29:16.296707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.558749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.587304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.478963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.289321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.141121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.027212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.854511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.752519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.707729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.541142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.478215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.344560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.224478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.112730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.997952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.898114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.704761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.534197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.443523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.336632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.638367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.627991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.518104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.330332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.180722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.067685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.899414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.794579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.747605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.584512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.519781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.385698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.264365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.155392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.038367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.942015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.745206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.575141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.484297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.377753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.715769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.666777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.556709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.371615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.219826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.106968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.943054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.904637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.787787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.626389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.562021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.427158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.371540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.199117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.078058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.983122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.784970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.616025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.526100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.416326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.792474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.704672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.594065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.410900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.257976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.146308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.985884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.945974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.825674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.668141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.601739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.471377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.409396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.240156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.116785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.020214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.823319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.654884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.565895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.459793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.850172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.747396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.635854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.453007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.300559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.188750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.031813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.991081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.868679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.714292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.646798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.521163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.451798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.286245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.160168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.063945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.866154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.697234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.610474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.500193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.890082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.786544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.673852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.493751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.338193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.228351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.075108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.033065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.907338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.757080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.688352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.569375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.490287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.328310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.199122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.101771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.905333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.738000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.650353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.540046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:28:59.931104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.828986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.712690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.533556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.380553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.267511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.118008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.076585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.948600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.799011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.729613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.612545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.530197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-07T10:29:10.947553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.821315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.663964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.547435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.369726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.277729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.128938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:17.009838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.401679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.293032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.166472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.011690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.906407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.730001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.615474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.575917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.414462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.348833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.214365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.094955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.987708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.865576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.704391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.585692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.409679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.318687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.170913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:17.054786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.500197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.334432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.207668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.054918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.946242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.771291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.661395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.620785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.457837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.392036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.257769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.138263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.030197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.909375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.816147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.624994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.451722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.360248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.213270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:17.098055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:00.546503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:01.375834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:02.248512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.098960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:03.987306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:04.814379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:05.707090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:06.664644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:07.500029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:08.435778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:09.302505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:10.182412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.072085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:11.954405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:12.857407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:13.666548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:14.493059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:15.402517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T10:29:16.254422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-07T10:29:17.183586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T10:29:17.438617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

row_numbercustomer_idsurnamecredit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryexitedage_squaredbalance_sqrtcredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography
0115634602Hargrave619FranceFemale4220.00111101348.88117640.0000006190.001.00.7567631.000000e+060.00000014.7380950.0476190.006108lowmiddle_agemediumnew650.83138976785.3117452565.0
1215647311Hill608SpainFemale41183807.86101112542.5801681289.4958726083436122.260.71.1467661.193206e-050.74467714.8292680.0243900.005402lowmiddle_agemediumnew650.83138975742.9751091400.0
2315619304Onio502FranceFemale428159660.80310113931.5711764399.57577515066705753.600.91.9560361.878983e-051.40137511.9523810.1904760.004406highmiddle_agemediumlong_standing650.83138976785.3117452879.0
3415701354Boni699FranceFemale3910.0020093826.63015210.00000013980.000.40.9691512.000000e+060.00000017.9230770.0256410.007450lowmiddle_agemediumnew650.83138974285.2890312376.0
4515737888Mitchell850SpainFemale432125510.8211179084.1001849354.2750638505396965.261.01.1456827.967441e-061.58705519.7674420.0465120.010748lowmiddle_agehighnew650.83138981636.037677958.0
5615574012Chu645SpainMale448113755.78210149756.7111936337.27700812905005254.320.71.7022061.758152e-050.75960414.6590910.1818180.004307lowmiddle_agemediumlong_standing650.27689278244.4922571883.0
6715592531Bartlett822FranceMale5070.0021110062.80025000.00000016440.001.20.5503162.000000e+060.00000016.4400000.1400000.081687lowmiddle_agehighlong_standing650.27689284924.807388260.0
7815656148Obinna376GermanyFemale294115046.74410119346.881841339.18540715043336355.461.12.0552973.476848e-050.96396912.9655170.1379310.003150lowadulthoodlowintermediate650.83138968990.3700571481.0
8915792365He501FranceMale444142051.0720174940.5001936376.89663010026250247.080.91.4408871.407944e-051.89551811.3863640.0909090.006685lowmiddle_agemediumintermediate650.27689278244.4922571875.0
91015592389H?684FranceMale272134603.8811171725.730729366.8840146843634304.761.01.1451307.429206e-061.87664725.3333330.0740740.009536lowadulthoodmediumnew650.27689272536.1457421790.0
row_numbercustomer_idsurnamecredit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryexitedage_squaredbalance_sqrtcredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography
9990999115798964Nkemakonam714GermanyMale33335016.6011053667.0801089187.1272297141155547.800.50.6579112.855788e-050.65247821.6363640.0909090.013304lowadulthoodhighnew650.27689276448.987534668.0
9991999215769959Ajuluchukwu597FranceFemale53488381.2111069384.7112809297.2897745974684204.130.50.9491961.131462e-051.27378511.2641510.0754720.008604lowseniormediumintermediate650.83138987894.0378381737.0
9992999315657105Chukwualuka726SpainMale3620.00110195192.40012960.0000007260.000.51.2259991.000000e+060.00000020.1666670.0555560.003719lowmiddle_agehighnew650.27689272727.0878952420.0
9993999415569266Rahman644FranceMale287155060.4111029179.520784393.7771076444341691.480.51.0139256.449099e-065.31401523.0000000.2500000.022070highadulthoodmediumlong_standing650.27689273159.736740721.0
9994999515719294Wood800FranceFemale2920.00200167773.5508410.00000016000.000.41.3388992.000000e+060.00000027.5862070.0689660.004768lowadulthoodhighnew650.83138968990.3700574209.0
9995999615606229Obijiaku771FranceMale3950.0021096270.64015210.00000015420.000.70.9813712.000000e+060.00000019.7692310.1282050.008009lowmiddle_agehighintermediate650.27689274285.2890312441.0
9996999715569892Johnstone516FranceMale351057369.61111101699.7701225239.5195405162007936.351.00.9871751.743083e-050.56410814.7428570.2857140.005074lowadulthoodmediumlong_standing650.27689277410.4067092573.0
9997999815584532Liu709FranceFemale3670.0010142085.58112960.0000007090.000.70.4604361.000000e+060.00000019.6944440.1944440.016847lowmiddle_agehighlong_standing650.83138972727.0878951032.0
9998999915682355Sabbatini772GermanyMale42375075.3121092888.5211764273.99874115443153163.020.71.2636862.663992e-050.80823018.3809520.0714290.008311lowmiddle_agehighnew650.27689276785.3117451148.0
99991000015628319Walker792FranceFemale284130142.7911038190.780784360.7530877923643998.120.50.9596697.683868e-063.40770228.2857140.1428570.020738lowadulthoodhighintermediate650.83138973159.736740938.0